Quality of life in opium-addicted patients with coronary artery disease as measured with WHOQOL-BREF

QUALITY OF LIFE IN OPIUM-ADDICTED PATIENTS WITH CORONARY ARTERY DISEASE AS MEASURED

WITH WHOQOL-BREF

MAHDI NAJAFI, MEHRDAD SHEIKHVATAN, ALI MONTAZERI & MAHMOOD SHEIKHFATOLLAHI

ABSTRACT

Objective: Several factors can infl uence the quality of life in patients with coronary artery disease (CAD). The goal of this research was to measure quality of life in addicted patients with coronary artery disease in order to assess the effect of coronary artery disease risk factors on their quality of life.Method: The WHOQOL-BREF questionnaire was completed through interviews with 275 patients who underwent isolated coronary artery bypass surgery in Tehran Heart Centre between May and September 2006.Results: No signifi cant differences were found in the mean scores of the four domains of quality of life between the addicted and non-addicted patients. Furthermore, the evaluation of QOL in the groups with CAD risk factors showed that the mean QOL domains were statistically similar between opium addicted and non-opium addicted patients. In the addicted group, men had a higher psychological health score than women. A previous history of myocardial infarction reduced the psychological score in this group. Also, in the addicted patients with a history of diabetes mellitus, social functioning was better than that of the non-diabetics.Conclusions: The different domains of quality of life in our opium-addicted and non-addicted patients with CAD were similar; and among all the major risk factors for coronary artery disease, only female gender and a previous history of myocardial infarction could infl uence quality of life in the opium-addicted patients.

Key words: coronary artery disease, quality of life, opium, risk factor, WHOQOL-BREF

INTRODUCTION

Coronary artery disease (CAD) is a chronic disease that affects a broad spectrum of the world population and is a common reason for medical visits and expenses. Several previous studies have indicated that CAD is responsible for 30% of all deaths worldwide each year (WHO, 2001) and is the fi rst cause of mortality in many developing countries. These countries contribute a greater share to the global burden of CAD than do developed countries (Whelton et al., 1995) in as much as 80%

International Journal of Social Psychiatry. Copyright © 2008 SAGE Publications (Los Angeles, London, New Delhi and Singapore) www.sagepublications.com DOI: 10.1177/0020764008093600

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2 INTERNATIONAL JOURNAL OF SOCIAL PSYCHIATRY

of these deaths occur in low-to-middle income countries of varying sizes (Bonow et al., 2002). CAD also accounts for 46% of overall mortality in Iran (Chinikar et al., 2006). Projections to the year 2020 also predict an expansion of the CAD epidemic to the developing world (Pearson, 1999).

Many facets of quality of life (QOL) may be affected in patients with the risk factors for CAD. Epidemiologic studies of differences between countries and regions have revealed several factors that infl uence QOL in these patients (Lopez, 1993). Among these factors, the effect of opium use in CAD patients is controversial. In some studies, opioid peptides have a role as cardioprotective factors (Azimzade-Sarwar et al., 2005); and in others, the adverse effects of opium consumption on coronary arteries have been defi ned (Sadeghian et al., 2007).

The Marmor et al. study indicated that long-term exposure to opioids can be associated with decreased severity of CAD and, hence, with a decreased incidence of fatal myocardial infarctions (MI) (Marmor et al., 2004). One possible explanation for this fi nding is that narcotics may decrease infl ammation (Sanderson et al., 1998), which is associated with atherogenesis and plaque disruption (Rady et al., 2002). As a result, long-term opioid exposure may decrease atherosclerosis directly. Be that as it may, the effects of opium in QOL of CAD patients have yet to be fully explored.

Furthermore, according to the protective role of opiates on pathophysiology of CAD in some studies and the adverse effect of these substances as a risk factor for CAD in some others, it seems that the impact of opium use on QOL in CAD patients would be considerable. The present study sought to measure QOL in addicted CAD patients who underwent coronary artery bypass surgery (CABG) so as to assess the effect of CAD risk factors on the QOL domains of these patients and compare this effect between addicted and non-addicted patients.

MATERIALS AND METHODS

In this study 275 patients who underwent isolated CABG in Tehran Heart Centre between May and September 2006 were entered. Among these 41 patients were addicted to opium (case group) and 234 patients were not (control group). A data manager proposed the WHOQOL-BREF questionnaire (Murphy et al., 2000; Orley, 1996) to the patients on admission to the surgical ward. The questionnaire was completed with interviews before the CABG. The WHOQOL-BREF defi nes QOL as participants’ perceptions of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concerns. QOL refers to a subjective evaluation that is embedded in a cultural, social and environmental context. This defi nition of QOL focuses upon respondents’ ‘perceived’ QOL and refl ects the effects of disease and health interventions on QOL.

The recognition of the multi-dimensional nature of QOL in the WHOQOL-BREF is based on a four-domain structure: (1) Physical health activities of daily living; (2) psychological body image and appearance; (3) social and personal relationships; and (4) environmental–fi nancial resources. A summation and calculation of the mean score for each domain was done. According to the methodology, we transformed the domain scores to a 0 to 100-point scale by using the WHOQOL transformation table (Murphy et al., 2000; Orley, 1996). A higher score on this questionnaire indicates a better QOL. A study by Nejat et al. demonstrated good-to-excellent reliability and ac-ceptable validity of this questionnaire in various groups of subjects in Iran (Nejat et al., 2006).

The patients’ medical history and early complications after surgery were collected through interviews and physical examinations before the beginning of operation. The following preoperative variables were included for analysis:


NAJAFI ET AL.: QUALITY OF LIFE IN OPIUM-ADDICTED PATIENTS 3

1. General characteristics: age; gender; body mass index (BMI); and education level (primary

education defi ned as primary school or less, secondary education characterized as secondary school level, and high education defi ned as university/college levels or equivalent) (Mayer et al., 2004).

2. Preoperative risk factors: current smoking history (patient regularly smokes a tobacco product/products one or more times a day or has smoked in the 30 days prior to admission) (Barrett-Connor et al., 2004); alcohol abuse (the use of alcohol despite recurrent adverse consequences) (Gary, 2007); opium dependence (according to the DSM-IV criteria for sub-stance dependence, daily regular use of substances) (ABSY, 2000); hypercholesterolemia (total cholesterol ≥ 5.0 mmol/l, HDL-cholesterol ≤ 1.0 mmol/l in men, or ≤ 1.1 mmol/l in women, triglycerides ≥ 2.0 mmol/l) (Wood et al., 1998); family history of CAD (fi rst-degree relatives before the age of 55 in men and 65 years in women) (Bartnik et al., 2004); hypertension (systolic blood pressure ≥ 140 mmHg and/or diastolic ≥ 90 mmHg and/or on anti-hypertensive treatment) (Chalmers et al., 1999); diabetes mellitus (symptoms of diabetes plus at least one of the following: plasma glucose concentration ≥ 200 mg/dl (11.1 mmol/l), fasting plasma glucose ≥ 126 mg/dl (7.0mmol/l), 2-hp ≥ 200 mg/dl (11.1 mmol/l)) (Kuzuya, 2002); cerebrovascular disease; and peripheral vascular disease.

3. Preoperative cardiac status: previous MI (an acute event with abnormal creatine phosphokinase and troponin levels); Euroscore; and functional class.

4. Preoperative homodynamic status: number of defected coronary vessels and left ventricular ejection fraction.

We considered two criteria to a complicated postoperative short-term outcome:

1. In-hospital postoperative complications (existence of at least one of: postoperative arrhyth-mias, wound infection, brain stroke and respiratory failure).

2. In-hospital mortality rate (sometimes termed operative mortality) defi ned as death in hospital before discharge (Edmunds et al., 1996).

Results were reported as mean ± standard deviation (SD) for the quantitative variables and per-centages for the categorical variables. The groups were compared using the student’s t-test for the continuous variables and the χ2 test (or Fisher’s exact test if required) or Mantel-Haenszel χ2 test for trend for the categorical variables. The data analyzer was anonymous and data collection and processing were approved by the institutional review board of Tehran Heart Centre. P values of 0.05 or less were considered statistically signifi cant. All the statistical analyses were performed using SPSS version 13 (SPSS Inc, Chicago, IL, US).

RESULTS

The mean age was higher in the non-addicted patients (p = 0.055), and the education level had a trend for being higher in the addicted ones (p = 0.061) (Table 1). Among the general risk factors for CAD, cigarette smoking (p < 0.0001), renal failure (p = 0.055) and previous MI (p = 0.009) were more observed in the addicted patients, whereas hypercholesterolemia (p = 0.013) and hypertension (p = 0.038) were more prevalent in the non-addicted patients. Functional class and the number of



involved coronary arteries were similar in the two groups. The mean of ejection fraction was more in the non-addicted patients, but the mean Euroscore was similar between the two groups. Among the common postoperative complications, wound infection (p = 0.059), arrhythmia (p = 0.008) and brain stroke (p = 0.022) were more common in the addicted patients, whereas respiratory failure and in-hospital mortality rate were similar.

The mean scores of WHOQOL-BREF domains in the addicted and non-addicted patients are summarized in Table 2. No signifi cant differences in the four mean domain scores of QOL between the addicted and non-addicted patients were found.

Table 1Comparison of preoperative characteristics and postoperative complications

between opium-addicted and non-addicted patients†

Characteristics Opium-addicted patients(n = 41)

Non-addicted patients(n = 234)

p value

Male gender 89.2 77.4 0.425Mean age (year) 57.1 ± 9.5 60.2 ± 8.8 0.055Body mass index (kg/m2) 25.4 ± 3.7 27.5 ± 4.4 0.002Education level Primary 53.8 61.9 0.061 Secondary 17.9 24.7 High 28.2 13.5Family history of CAD‡ 55.0 44.4 0.216Current cigarette smoking 82.9 29.5 < 0.001Hyperlipidemia 51.2 70.9 0.013Hypertension 34.1 51.7 0.038Cerebrovascular disease 4.9 4.3 0.696Diabetes mellitus 34.1 43.6 0.259Peripheral vascular disease 14.6 21.4 0.323Last creatinine (mmol/l) 1.36 ± 0.23 1.29 ± 0.21 0.055Previous myocardial infarction 68.3 46.2 0.009Ejection fraction (%) 45.0 ± 9.5 50.0 ± 9.5 0.003Functional class I 39.0 32.5 0.709 II 46.3 52.1 III 14.6 15.4 Euroscore 2.49 ± 2.58 2.34 ± 2.22 0.730Number of defected vessels One 2.4 3.8 0.898 Two 24.4 23.1 Three 73.2 73.1Post-operative complications Wound infection 4.9 0.4 0.059 Arrhythmias 53.7 32.2 0.008 Respiratory failure 19.5 14.1 0.370 Brain stroke 4.9 0.0 0.022 Post-operative mortality 2.4 0.4 0.156

† Data are presented as percentage or mean ± SD ‡ Coronary artery disease



Comparison of the four WHOQOL-BREF subscales between opium-addicted patients with CAD risk factors and non-addicted patients without CAD risk factors indicated that no signifi cant differences were found between the two groups (Table 3). Furthermore, the evaluation of QOL in the groups with CAD risk factors showed that the mean QOL domains were statistically similar between opium addicted and non-opium addicted patients (Table 4).

We also considered the roles of CAD risk factors on the QOL domain scores in the opium-addicted groups and found that the mean of the physical domain score in the patients with a history of cigarette smoking was lower than that of the other patients (56.0 ± 9.5 vs 64.5 ± 4.8, p = 0.003). The addicted men had a psychological health score than the females (59.6 ± 9.8 vs 51.7 ± 5.7, p = 0.047). Previous history of MI reduced the psychological score of QOL in the addicted group (MI group, 59.2 ± 9.0 vs non-MI group, 62.6 ± 9.9, p = 0.048). Also, in the opium-addicted patients with a history of diabetes mellitus, social functioning was better than that of the non-diabetics (67.5 ± 14.8 vs 54.4 ± 18.6, p = 0.036). None of the CAD risk factors infl uenced the environ-mental domain score.

DISCUSSION

Although the long-term benefi ts of opioids in terms of pain relief, functional capacity and health-related QOL still remain to be proven, serious long-term consequences such as addiction, opioid-induced hyperalgesia, cognitive disorders and suppression of the immune and reproductive systems have been satisfactorily described (Højsted & Sjøgren, 2007).

This study was designed to evaluate QOL among CAD patients who were addicted to opiates and compare it with that of non-addicted patients. We found that the mean scores of the WHOQOL-BREF domains in our addicted and non-addicted patients were similar. In some studies, while the physical functioning of adult substance abusers was similar to the levels for patients, their mental functioning was much lower (Smith & Larson, 2003). Some other studies have, however, reported that patients with opiate dependence show signifi cantly poorer QOL in the physical, psychological, and social domains with respect to healthy participants (Bizzarri et al., 2005; Millson et al., 2004).

Differences between the results of the present study and other studies (higher QOL scores in non-addicted patients) might be due to the fact that in some studies, addicted patients were also suffering from other concomitant psychiatric disorders (Bizzarri et al., 2005; Sbrana et al., 2005)

Table 2Comparison of WHOQOL-BREF subscales between opium-addicted and non-addicted patients†

Characteristics Opium-addicted patients (n = 41)

Non-addicted patients (n = 234)

p value

Physical domain score 57.5 ± 9.4 56.1 ± 10.6 0.401Psychological domain score 58.2 ± 9.6 58.0 ± 11.7 0.881Social domain score 58.6 ± 18.3 59.6 ± 17.4 0.778Environmental domain score 55.5 ± 14.9 56.5 ± 14.0 0.689Overall score 57.8 ± 10.3 57.5 ± 9.9 0.853

† Data are presented as mean ± SD



and/or the effects of opioids were evaluated as a treatment protocol for pain relief (Dillie et al., 2008). Furthermore, a few studies on the evaluation of QOL in CAD patients using the WHOQOL-BREF questionnaire show that the use of this new tool may infl uence the results of QOL measurements of the patients with CAD, especially opium-addicted groups. In addition, because the activities of daily living can be limited due to chest pain, chest tightness and/or shortness of breath (Spertus et al., 1995), it seems that the effects of opium use on the improvement of different aspects of QOL can be related to its pain-relief properties (Ballantyne, 2007).

In the present study, the psychological domain score of QOL in women was lower than that in men. The modulator effects of endogenous opioid activity on gonadotropin-releasing hormone receptors and the release of luteinizing hormone at the pituitary level have been proved (Chao et al., 1986). In addition, more recent epidemiologic data have demonstrated that women’s hormone changes put them at a higher risk for developing psychological disorders, especially when compared with their risk during premenopausal years (Soares, 2007). Therefore the regularity of gonadotropin releasing can be disrupted by opium use and women may be susceptible to psychological disorders. These fi ndings can be the main reason for the lower psychological scores of QOL in women.

Table 3Comparison of WHOQOL-BREF subscales between opium-addicted patients with CAD

risk factors and non-addicted patients without CAD risk factors†

Risk factor PH PS SO EN

Obese addicted patients (N = 6) 60.67 ± 9.48 57.33 ± 10.07 66.67 ± 16.29 62.83 ± 14.39Non-obese non-addicted patients (N = 176) 55.72 ± 10.84 57.45 ± 11.17 59.99 ± 17.08 56.42 ± 13.66p value 0.272 0.981 0.347 0.260Addicted patients with FH (+) (N = 22) 57.00 ± 8.68 58.50 ± 9.77 60.39 ± 20.34 56.68 ± 12.78Non-addicted patients with FH (–) (N = 130) 56.00 ± 11.15 57.67 ± 12.04 59.87 ± 17.47 56.36 ± 13.93p value 0.944 0.982 0.947 0.774Addicted patients with CS (+) (N = 34) 56.06 ± 9.50 58.12 ± 9.58 56.68 ± 18.40 54.56 ± 15.39Non-addicted patients with CS (–) (N = 164) 55.68 ± 11.05 57.70 ± 11.53 58.98 ± 17.66 56.38 ± 14.01p value 0.854 0.842 0.544 0.510Addicted patients with HLP (+) (N = 21) 57.10 ± 9.51 58.14 ± 10.30 57.29 ± 18.92 52.63 ± 14.09Non-addicted patients with HLP (–) (N = 68) 54.56 ± 10.81 56.13 ± 11.31 58.77 ± 19.44 56.29 ± 13.92p value 0.337 0.469 0.780 0.315Addicted patients with HTN (+) (N = 14) 60.50 ± 6.01 58.50 ± 11.37 63.09 ± 17.31 57.42 ± 13.36Non-addicted patients with HTN (–) (N = 112) 55.42 ± 11.01 57.39 ± 11.02 58.48 ± 18.43 55.28 ± 14.54p value 0.093 0.724 0.418 0.609

Addicted patients with DM (+) (N = 14) 60.21 ± 10.31 58.14 ± 11.85 67.55 ± 14.80 50.54 ± 9.05Non-addicted patients with DM (–) (N = 132) 55.78 ± 10.52 57.73 ± 12.17 59.34 ± 17.21 56.64 ± 13.39p value 0.136 0.903 0.129 0.111Addicted patients with recent MI (+) (N = 28) 57.57 ± 9.81 56.25 ± 9.00 58.08 ± 15.14 55.15 ± 15.10Non-addicted patients with recent MI (–) (N = 125)

55.35 ± 11.82 58.10 ± 12.31 60.82 ± 18.07 57.21 ± 13.92

p value 0.358 0.365 0.489 0.501

PH: physical domain score; PS: psychological domain score; SO: social domain score; EN: environmental domain score; FH: family history of coronary artery disease; CS: history of current cigarette smoking; HLP: history of hyperlipidemia; HTN: history of hypertension; DM: history of diabetes mellitus; MI: myocardial infarction† Data are presented as mean ± SD



In addition, the psychological domain score in our addicted patients with a history of MI was lower than that in non-addicts. Several studies have demonstrated that MI is a main predictor for psychological disorders, especially depression in patients with CAD (Carney et al., 1990; Schleifer et al., 1991; Watkins et al., 2003). It has also been estimated that 65% of patients with a history of acute MI report symptoms of mood disorders, such that major depression is present in 15–22% of these patients (Guck et al., 2001).

In our study, social functioning was better in the diabetic patients than that in the non-diabetics. In a population-based study by Glass et al., increasing levels of social activities were signifi cantly related to decreased mortality and morbidity during long follow-up in diabetic patients (Glass et al., 1999). Another study reported signifi cant relationships between increased participation in social activities and lower risk of physical disability in elderly diabetic patients (Mendes de Leon et al., 2003). Nonetheless, it seems that the better social activity in the diabetic patients in our study

Table 4WHOQOL-BREF subscales between opium-addicted and non-addicted patients with CAD risk factors†

Risk factor PH PS SO EN

Obesity Addicted patients 60.67 ± 9.48 57.33 ± 10.07 66.67 ± 16.29 62.83 ± 14.39 Non-addicted patients 57.45 ± 9.82 59.75 ± 13.10 58.40 ± 18.59 57.04 ± 15.40 p value 0.448 0.663 0.301 0.381Family history of CAD Addicted patients 57.00 ± 8.68 58.50 ± 9.77 60.39 ± 20.34 56.68 ± 12.78 Non-addicted patients 56.31 ± 9.91 58.45 ± 11.27 59.28 ± 17.45 56.83 ± 14.32 p value 0.765 0.985 0.809 0.966Current cigarette smoking Addicted patients 56.06 ± 9.50 58.12 ± 9.58 56.68 ± 18.40 54.56 ± 15.39 Non-addicted patients 57.21 ± 9.48 58.78 ± 12.11 61.08 ± 16.89 57.03 ± 14.31 p value 0.566 0.782 0.265 0.433Hyperlipidemia Addicted patients 57.10 ± 9.51 58.14 ± 10.30 57.29 ± 18.92 52.63 ± 14.09 Non-addicted patients 56.80 ± 10.49 58.79 ± 11.79 59.95 ± 16.56 56.68 ± 14.18 p value 0.903 0.810 0.536 0.239Hypertension Addicted patients 60.50 ± 6.01 58.50 ± 11.37 63.09 ± 17.31 57.42 ± 13.36 Non-addicted patients 56.80 ± 10.23 58.59 ± 12.30 60.68 ± 16.42 57.77 ± 13.58 p value 0.188 0.979 0.644 0.932Diabetes mellitus Addicted patients 60.21 ± 10.31 58.14 ± 11.85 67.55 ± 14.80 50.54 ± 9.05 Non-addicted patients 56.60 ± 10.76 58.39 ± 11.08 59.94 ± 17.78 56.49 ± 14.98 p value 0.239 0.939 0.175 0.166Recent myocardial infarction Addicted patients 57.57 ± 9.81 56.25 ± 9.00 58.08 ± 15.14 55.15 ± 15.10 Non-addicted patients 57.03 ± 9.00 57.91 ± 10.98 58.28 ± 16.69 55.83 ± 14.28 p value 0.781 0.463 0.959 0.830

PH: physical domain score; PS: psychological domain score; SO: social domain score; EN: environmental domain score† Data are presented as mean ± SD



is an unusual fi nding. Furthermore, the fact that the degree of social activities depends on such various factors as lifestyle, occupational status, neighborhood composition, physical environment, perception of health benefi ts, and physical and mental health of the participants (Satariano et al., 2000; Satariano et al., 2002; Wilcox et al., 2000) renders the consideration of these factors im-portant in the interpretation of social functioning in diabetic patients, especially opiate users.

In the present study, we did not demonstrate the impact of CAD risk factors on the environ-mental domain in opiates users; nevertheless, it is clear that this domain with special reference to socio-economic status has a strong relationship with the dietary pattern and blood lipids in cases of coronary heart disease. It can be concluded, therefore, that environmental factors can probably be affected by these patterns. Moreover, it is essential that the effi cacy of the WHOQOL-BREF questionnaire be assessed in future studies concerning opium users with CAD.

Some limitations of the present study need to be addressed. First, the sample size of the study was small and the relationships may be statistically signifi cant in greater sample size. In addition, results of the study should be considered with other QOL assessment tools, especially specifi c questionnaires in CAD groups, because it has been shown that the WHOQOL-BREF measures only global QOL and cannot measure health-related QOL, which should be evaluated in CAD patients (Huang et al., 2006).

In summary, the different domains of QOL in our opium-addicted and non-addicted patients with CAD were similar; and among all major risk factors for CAD, only female gender and pre-vious history of MI could infl uence QOL among the addicted patients.

ACKNOWLEDGEMENTS

This research project was supported by Medical Sciences/University of Tehran. We wish to thank all the researchers who took part in this study for their kind assistance, especially Dr Shahin Akhoundzadeh and Dr Soheil Saadat for their valuable technical assistance and statist-ical analysis.

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Mahdi Najafi , Assistant Professor, Tehran Heart Centre, Medical Sciences/University of Tehran, Iran.

Mehrdad Sheikhvatan, Assistant Research Fellow, Tehran Heart Centre, Medical Sciences/University of Tehran, Iran.

Ali Montazeri, Associate Professor, Tehran Heart Centre, Medical Sciences/University of Tehran, Iran.

Mahmood Sheikhfatollahi, Biostatistician, Tehran Heart Centre, Medical Sciences/University of Tehran, Iran.

Correspondence to: [email protected]


Documents

Quality of life in opium-addicted patients with coronary artery disease as measured with WHOQOL-BREF